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Gas Analysis by In Situ Combustion in Heavy‐Oil Recovery Process: Experimental and Modeling Studies
Author(s) -
Ahmadi MohammadAli,
Masumi Mohammad,
Kharrat Riaz,
Mohammadi Amir H.
Publication year - 2014
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201300155
Subject(s) - particle swarm optimization , combustion , artificial neural network , enhanced oil recovery , process engineering , process (computing) , genetic algorithm , in situ , computer science , environmental science , engineering , petroleum engineering , chemistry , artificial intelligence , machine learning , organic chemistry , operating system
Enormous efforts have been made to facilitate produced‐gas analyses by in situ combustion implication in heavy‐oil recovery processes. Robust intelligence‐based approaches such as artificial neural network (ANN) and hybrid methods were accomplished to monitor CO 2 /O 2 /CO. Implemented optimization approaches like particle swarm optimization (PSO) and hybrid approach focused on pinpointing accurate interconnection weights through the proposed ANN model. Solutions acquired from the developed approaches were compared with the pertinent experimental in situ combustion data samples. Implication of hybrid genetic algorithm and PSO in gas analysis estimation can lead to more reliable in situ combustion quality predictions, simulation design, and further plans of heavy‐oil recovery methods.